The AAPS Journal

, Volume 13, Issue 4, pp 508–518 | Cite as

Bayesian Quantitative Disease–Drug–Trial Models for Parkinson’s Disease to Guide Early Drug Development

Research Article

Abstract

The problem we have faced in drug development is in its efficiency. Almost a half of registration trials are reported to fail mainly because pharmaceutical companies employ one-size-fits-all development strategies. Our own experience at the regulatory agency suggests that failure to utilize prior experience or knowledge from previous trials also accounts for trial failure. Prior knowledge refers to both drug-specific and nonspecific information such as placebo effect and the disease course. The information generated across drug development can be systematically compiled to guide future drug development. Quantitative disease–drug–trial models are mathematical representations of the time course of biomarker and clinical outcomes, placebo effects, a drug’s pharmacologic effects, and trial execution characteristics for both the desired and undesired responses. Applying disease–drug–trial model paradigms to design a future trial has been proposed to overcome current problems in drug development. Parkinson’s disease is a progressive neurodegenerative disorder characterized by bradykinesia, rigidity, tremor, and postural instability. A symptomatic effect of drug treatments as well as natural rate of disease progression determines the rate of disease deterioration. Currently, there is no approved drug which claims disease modification. Regulatory agency has been asked to comment on the trial design and statistical analysis methodology. In this work, we aim to show how disease–drug–trial model paradigm can help in drug development and how prior knowledge from previous studies can be incorporated into a current trial using Parkinson’s disease model as an example. We took full Bayesian methodology which can allow one to translate prior information into probability distribution.

Key words

Bayesian method drug development prior knowledge quantitative disease–drug–trial model 

References

  1. 1.
    Elias T, Gordian M, Singh N, Zemmel R. Why products fail in phase III. In Vivo. 2006;24:49–54.Google Scholar
  2. 2.
    Hooper M, Amsterdam JD. Do clinical trials reflect drug potential? A review of FDA evaluation of new antidepressants. Presented at NCDEU 39th Annual Meeting, Boca Raton, FL. 11–14 June, 1998.Google Scholar
  3. 3.
    Bhattaram VA, Booth BP, Ramchandani RP, Beasley BN, Wang Y, Tandon V, et al. Impact of pharmacometrics on drug approval and labeling decisions: a survey of 42 new drug applications. AAPS J. 2005;7:E503–12.PubMedCrossRefGoogle Scholar
  4. 4.
    Bhattaram VA, Bonapace C, Chilukuri DM, Duan JZ, Garnett C, Gobburu JV, et al. Impact of pharmacometric reviews on new drug approval and labeling decisions—a survey of 31 new drug applications submitted between 2005 and 2006. Clin Pharmacol Ther. 2007;81:213–21.PubMedCrossRefGoogle Scholar
  5. 5.
    Gobburu J. Disease model. Clin Adv Hematol Oncol. 2008;6:241–42.PubMedGoogle Scholar
  6. 6.
    Gobburu J, Lesko LJ. Quantitative disease, drug and trial models. Annu Rev Pharmacol Toxicol. 2009;49:291–301.PubMedCrossRefGoogle Scholar
  7. 7.
    Zhang L, Sinha V, Forgue ST, Callies S, Ni L, Peck R, et al. Model-based drug development: the road to quantitative pharmacology. J PKPD. 2006;33(3):369–93.Google Scholar
  8. 8.
    Bhattaram VA, Siddiqui O, Kapcala L, Gobburu J. Endpoints and analyses to discern disease-modifying drug effects in early Parkinson’s disease. AAPS J. 2009;11:456–64.PubMedCrossRefGoogle Scholar
  9. 9.
    Ibrahim J, Chen M. Power prior distributions for regression models. Stat Sci. 2000;15:46–60.CrossRefGoogle Scholar
  10. 10.
    Holford NH, Peace KE. Methodologic aspects of a population pharmacodynamic model for cognitive effects in Alzheimer patients treated with tacrine. Proc Natl Acad Sci USA. 1992;89:11466–70.PubMedCrossRefGoogle Scholar
  11. 11.
    Chan PL, Holford NH. Drug treatment effects on disease progression. Annu Rev Pharmacol Toxicol. 2001;41:625–59.PubMedCrossRefGoogle Scholar
  12. 12.
    Holford NH, Chan PL, Nutt JG, Kieburtz K, Shoulson I. Disease progression and pharmacodynamics in Parkinson disease—evidence for functional protection with levodopa and other treatments. J Pharmacokinet Pharmacodyn. 2006;33:281–311.PubMedCrossRefGoogle Scholar
  13. 13.
    Parkinson Study Group. Levodopa and the progression of Parkinson’s disease. NEJM. 2004;351:2498–508.CrossRefGoogle Scholar
  14. 14.
    Parkinson Study Group. A controlled trial of rasagiline in early Parkinson disease: the TEMPO study. Arch Neurol. 2002;59(12):1937–43.CrossRefGoogle Scholar
  15. 15.
    Guimaraes P, Kieburtz K, Goetz CG, Elm J, Palesch Y, Huang P, et al. Nonlinearity of Parkinson’s disease progression: implication for sample size calculation in clinical trials. Clin Trials. 2005;2:509–18.PubMedCrossRefGoogle Scholar
  16. 16.
    Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences (with discussion). Stat Sci. 1992;7:457–511.CrossRefGoogle Scholar
  17. 17.
    Gelman A, Carlin J, Stern H, Rubin D. Bayesian data analysis. 1st ed. London: Chapman & Hall/CRC; 1995.Google Scholar
  18. 18.
    Wakefield JC. The Bayesian analysis of population pharmacokinetic models. J Am Stat Ass. 1996;91:61–76.Google Scholar
  19. 19.
    Wakefield JC. Bayesian individualisation via sampling-based methods. J Pharmacokinet Biopharm. 1996;24:103–31.PubMedCrossRefGoogle Scholar
  20. 20.
    Spiegelhalter DJ, Freedman L, Parmar MKB. Bayesian approaches to randomized clinical trials (with discussion). J Roy Stat Soc Ser A. 1994;157:357–415.CrossRefGoogle Scholar
  21. 21.
    Gilks WR, Richardson S, Spiegelhalter DJ. Markov Chain Monte Carlo in practice. London: Chapman and Hall; 1996.Google Scholar
  22. 22.
    Chaloner K. Elicitation of prior distributions. In: Berry DA, Stangl DK, editors. Bayesian biostatistics. New York: Marcel Dekker; 1996. p. 141–56.Google Scholar
  23. 23.
    Chaloner K, Rhame FS. Quantifying and documenting prior beliefs in clinical trials. Stat Med. 2001;20:581–600.PubMedCrossRefGoogle Scholar
  24. 24.
    Kadange JB, Wolfson LJ. Experiences in elicitation. The Statistician. 1998;47:3–19.CrossRefGoogle Scholar
  25. 25.
    O’Hagan A. Eliciting expert beliefs in substantial practical applications. The Statistician. 1998;47:21–35.CrossRefGoogle Scholar
  26. 26.
    Press SJ. Subjective and objective Bayesian statistics. 2nd ed. New Jersey: Wiley; 2003.Google Scholar
  27. 27.
    Chen M, Ibrahim J. The relationship between the power prior and hierarchical models. Bayesian Anal. 2006;1:551–74.CrossRefGoogle Scholar
  28. 28.
    Chen H, Ibrahim J, Shao Q, Weiss RE. Prior elicitation for model selection and estimation in generalzied linear mixed models. J Stat Plan Inf. 2003;111:57–76.CrossRefGoogle Scholar
  29. 29.
    Neelon B, O’Malley AJ. Bayesian analysis using power priors with application to pediatric quality of care. J Biomet Biostat. 2010;1:1–9.CrossRefGoogle Scholar
  30. 30.
    Gelman A, Meng A, Stern H. Posterior predictive assessment of model fitness via realized discrepancies. Stat Sin. 1996;6:733–807.Google Scholar
  31. 31.
    Meng X. Posterior predicitve p values. Ann Stat. 1994;22:1142–60.CrossRefGoogle Scholar
  32. 32.
    Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. New Jersey: Wiley; 2002.Google Scholar
  33. 33.
    Diggle P, Kenward MG. Informative drop-out in longitudinal data analysis. Applied Statistics. 1994;43:49–73.CrossRefGoogle Scholar
  34. 34.
    Little RJA. Modeling the drop-out mechanism in repeated-measures studies. J Am Stat Assoc. 1995;90:1112–21.CrossRefGoogle Scholar
  35. 35.
    NONMEM: nonlinear-mixed effects modelling, Icon Development Solutions, Ellicot City, MD, USA.Google Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2011

Authors and Affiliations

  1. 1.Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Science, Center for Drug Evaluation and ResearchFood and Drug AdministrationSilver SpringUSA

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